Mavrogeni, Mikaella;
Longley, Paul;
Van Dijk, Justin;
(2023)
The use of in-app data to drive geodemographic classification of activity patterns.
In:
Proceedings of the 31st Conference of GIS Research UK (GISRUK).
GIS Research UK (GISRUK): Glasgow, UK.
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Abstract
We use location data from multiple mobile phone applications to describe daily, weekly, seasonal and annual activity patterns. Geodemographics, or ‘the analysis of people by where they live’, provides an organising framework, extended to represent the ways in which neighbourhood residents interact with workplaces, recreational and leisure destinations and transport infrastructure. We evaluate how in-app location data can be incorporated into geodemographic analysis to better understand the flux of activity patterns that characterise densely populated areas throughout the day. Limitations and net benefits of in-app location data are critically assessed to evaluate the ways in which activity-based geodemographics are robust, effective and safe to use when characterising the population at large.
Type: | Proceedings paper |
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Title: | The use of in-app data to drive geodemographic classification of activity patterns |
Event: | 31st GISRUK Conference 2023 |
Location: | Glasgow, UK |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.5281/zenodo.7839567 |
Publisher version: | https://doi.org/10.5281/zenodo.7839567 |
Language: | English |
Additional information: | This is an Open Access article published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | geodemographics, big data, temporal analytics, in-app data, geospatial |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography |
URI: | https://discovery.ucl.ac.uk/id/eprint/10193189 |
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